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A note on the asymptotics of a stochastic vector difference equation

随机向量差分方程渐近性质的一则札记

This note analyses a necessary condition for asymptotic normality of the maximum likelihood estimator in a stationary stochastic vector difference equation. It is shown that this condition is satisfied if the error variance matrix is positive definite.

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Symmetry, 0-1 Matrices and Jacobians: A Review

对称性、0–1 矩阵与雅可比矩阵:综述

In this paper we bring together those properties of the Kronecker product, the vec operator, and 0-1 matrices which in our view are of interest to researchers and students in econometrics and statistics. The treatment of Kronecker products and the vec operator is fairly exhaustive; the treatment of 0–1 matrices is selective. In particular we study the “commutation” matrix K (defined implicitly by K vec A = vec A′ for any matrix A of the appropriate order), the idempotent…

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The Commutation Matrix: Some Properties and Applications

交换矩阵:若干性质与应用

The commutation matrix $K$ is defined as a square matrix containing only zeroes and ones. Its main properties are that it transforms vecA into vecA', and that it reverses the order of a Kronecker product. An analytic expression for $K$ is given and many further properties are derived. Subsequently, these properties are applied to some problems connected with the normal distribution. The expectation is derived of $\varepsilon' A\varepsilon\cdot\varepsilon' B\varepsilon\cdot\varepsilon'C\varepsilon$…

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Bounds for the Bias of the LS Estimator of @s^2 in the Case of a First-Order (Positive) Autoregressive Process when the Regression Contains a Constant Term

含常数项回归中正系数一阶自回归情形下 σ² 最小二乘估计量的偏差界

H. Neudecker, Bounds for the Bias of the LS Estimator of σ 2 in the Case of a First-Order (Positive) Autoregressive Process when the Regression Contains a Constant Term, Econometrica, Vol. 46, No. 5 (Sep., 1978), pp. 1223-1226

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Bounds for the Bias of the Least Squares Estimator of @s^2 in the Case of a First-Order Autoregressive Process (Positive Autocorrelation)

正自相关一阶自回归过程中 σ² 最小二乘估计量的偏差界

H. Neudecker, Bounds for the Bias of the Least Squares Estimator of @s^2 in the Case of a First-Order Autoregressive Process (Positive Autocorrelation), Econometrica, Vol. 45, No. 8 (Nov., 1977), p. 2006

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A Note on Blus Estimation

BLUS估计的一则札记

For the classical linear model, Theil and Koerts developed the so-called best linear unbiased scalar-variance-matrix (BLUS) estimator. In this note a theorem on the variance matrix of the BLUS estimation errors will be proved. A new derivation will be given for “price” and “efficiency” of the estimator, as defined by Koerts.

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Some Theorems on Matrix Differentiation with Special Reference to Kronecker Matrix Products

矩阵微分的若干定理:特别关注 Kronecker 积

Matrix differentiation, the procedure of finding partial derivatives of the elements of a matrix function with respect to the elements of the argument matrix, is the subject matter of this paper. The method followed here amounts to linking the differentials of the matrix function and the argument matrix, and then identifying matrices of partial derivatives. The basic assumption made is mathematical independence of the elements of the argument matrix. Matrix functions are…

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A Note on BLUS Estimation

关于 BLUS 估计的札记

For the classical linear model, Theil and Koerts developed the so-called best linear unbiased scalar-variance-matrix (BLUS) estimator. In this note a theorem on the variance matrix of the BLUS estimation errors will be proved. A new derivation will be given for “price” and “efficiency” of the estimator, as defined by Koerts.

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